基于局部保持投影的掌纹识别  

Palmprint Recognition Based on Locality Preserving Projection

作　　者：郭金玉[1,2] 苑玮琦[1] 

机构地区：[1]沈阳工业大学视觉检测技术研究所,辽宁沈阳110023 [2]沈阳化工学院信息工程学院,辽宁沈阳110142

出　　处：《光学学报》2008年第10期1920-1924,共5页Acta Optica Sinica

基　　金：国家自然科学基金(60472088)资助课题

摘　　要：为了保持掌纹空间的局部结构,运用局部保持投影(LPP)方法进行掌纹识别。在小样本图像识别中,特征方程矩阵存在奇异性。传统的解决方法是运用主元分析(PCA)获得原样本的低维特征子空间,在该空间中运用LPP进行特征提取。由于PCA和LPP的投影标准本质上是不同的,PCA降维时丢失许多重要的判别信息。为了解决这个问题,提出运用三级小波变换、图像下抽样、图像分块求平均值三种方法降低掌纹空间的维数,在低维图像上应用LPP提取局部特征,计算特征矢量间的余弦距离进行掌纹匹配。运用PolyU掌纹图像库进行测试,结果表明,该算法的识别性能均优于PCA和PCA+LPP。特征提取和匹配总时间小于0.1 s,具有快速、有效、易于实现等优点。In order to preserve the local structure of the image space, locality preserving projection （LPP） is applied to palmprint recognition. In small-size-sample cases such as image recognition, the matrix of the eigenvalue equation is singular. The traditional solution is to utilize the principal component analysis （PCA） as a pre-processing step aiming to reduce the dimensionality of the palmprint space, then LPP is applied to extract feature. Since the projection criterion of the PCA and that of LPP are essentially different, the pre-processing step to reduce the dimensionality using the PCA could result in the loss of some important discriminatory information. To solve the above problem, the three-methods, the three-level wavelet transform, image down-sample, and the mean of block segmentation, are presented to reduce palmprint space dimensionality. Then LPP is used to extract the local features. The cosine distance between two feature vectors is calculated to match palmprint. The three algorithms are tested in PolyU plmprint database. The results show that the recognition performance of the algorithm exceeds PCA and PCA＋ LPP. The all time is less than 0. 1 s including feature extraction and matching time, so it has the advantages of quickness, high efficiency, and easy realization.

关 键 词：掌纹识别 局部保持投影 小波变换 主元分析 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]